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Record W4311226121 · doi:10.53730/ijhs.v6ns10.13753

effect of progressive muscle relaxation technique for anxiety among menopause women

2022· article· en· W4311226121 on OpenAlexaboutno aff
Sunaryo Joko Waluyo, Siti Nur Solikah, Ratna Kusuma Astuti, Rahayu Setyaningsih, Undari Nurkalis

Bibliographic record

VenueInternational Journal of Health Sciences · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Behavioral Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMenopauseAnxietyRelaxation (psychology)Randomized controlled trialProgressive muscle relaxationMedicinePhysical therapyGynecologyPsychologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

This study aimed to o analyze the effect of muscle relaxation for anxiety among menopause women. This study is a systematic review and meta-analysis. The articles used in this study were obtained from three database, namely PubMed, Science Direct, and Google Scholar. The articles included are full-text article with a study design of randomized controlled trial from 2013 to 2022. Articles were analyzed using the Review Manager 5.3 application. A total of 9 articles from Asia (India, Taiwan & Turkey), Africa (Ethiopia), Europe (Spanyol), and North America (Canada). The data collected showed that anxiety in meno­pausal women who do PMRT will decrease by 0.37 units compared to menopausal women who do not do PMRT, and the results were statistically significant (SMD= -0.37; 95% CI= -0.63 to -0.12; p= 0.004).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.013
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.418
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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